projection-patterns

To turn event streams into queryable read models, you apply deterministic projection logic to transform events into denormalized views. This skill provides templates for Python, SQL, and Elasticsearch to build and maintain these read models.

Building projections for event-sourced architectures requires applying structured projector templates that support idempotent updates. This approach ensures deterministic projection logic across multiple data stores like SQL and Elasticsearch for real-time dashboards.

Yes, the projection architecture supports various projection types including Live, Catchup, Persistent, and Inline. These types ensure read models stay up to date as new events arrive and are processed within the event-sourced system.

Yes, you can use the included templates with Elasticsearch, PostgreSQL, and Python. They provide ready-to-adapt code to implement projections, such as building a product catalog search projection that updates a cache or table.

To ensure idempotent updates when processing event streams, the projection logic enforces deterministic processing rules. This prevents duplicate data entries if the same event is processed multiple times during catchup or recovery.

You need projections for event-sourcing read sides because event streams are optimized for writes, not queries. Projections transform these streams into denormalized views, enabling fast, scalable querying for analytics and dashboards.